I am currently the Alfred J. Verrecchia Endowed Assistant Professor in the College of Business at URI. Before this role, I was an assistant professor in the Department of Information Systems and Analytics at Bryant University. I earned my MSc and PhD in Statistics from the University of British Columbia under the guidance of Prof. Alexandre Bouchard-Côté.

I have held postdoctoral positions at UMass Amherst, Northeastern University, and Harvard Medical School, with a focus on data science, machine learning, business analytics, and healthcare informatics. My research interests include Bayesian Statistics, machine learning methodologies, deep learning, and healthcare analytics. I am passionate about teaching and excel in undergraduate and graduate courses on machine learning, data visualization, and statistics. During my time at Bryant University, I coordinated undergraduate healthcare analytics programs and chaired the Analytics Without Borders Conference in 2024. In my free time, I enjoy playing with my four-year-old daughter and exploring new destinations through travel.

Interests

  • Deep Learning
  • Business Analytics
  • Explainable AI

Education

  • PhD in in Statistics, 2018

    UBC

  • MSc in Statistics, 2013

    UBC

  • BSc in Mathematics, 2011

    ZheJiang University

Experience

 
 
 
 
 

Assistant Professor

University of Rhode Island

Aug 2024 – Present Rhode Island, US
 
 
 
 
 

Assistant Professor

Bryant University

Jan 2022 – Jul 2024 Rhode Island, US
 
 
 
 
 

Tripods Postdoctoral Research Associate

UMass Amherst

Dec 2020 – Dec 2021 Amherst, US
 
 
 
 
 

Joint Sponsored Research Fellow

Brigham and Women’s Hospital of Harvard Medical School

Jan 2019 – Dec 2020 Boston, US
Groupwise and individual feature selection in deep learning using knockoff construction with applications to Chronic Obstructive Pulmonary Disease (COPD). Developed novel machine learning methodology to improve prediction accuracy of exacerbations in COPD.
 
 
 
 
 

Postdoctoral Researcher

Department of Electrical and Computer Engineering, Northeastern University

Jan 2019 – Dec 2020 Boston, US
Proposed a nonparametric Bayesian modelling framework combined with deep learning and developed a novel online algorithm for deep representation and clustering for streaming data. Developed a novel instancewise feature selection method for model interpretation.

Projects

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Deep-gKnock

Deep-gKnock: nonlinear group-feature selection with controlled group-wise False Discovery Rate

Talks / Workshops

Recent Publications

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Deep bayesian unsupervised lifelong learning
Improved prediction of smoking status via isoform-aware RNA-seq deep learning models
Instance-wise feature grouping
Bayesian analysis of continuous time Markov chains with application to phylogenetic modelling
Study on RMB number recognition based on genetic algorithm artificial neural network

Contact

Send me a note

  • Ballentine Hall, 7 Lippitt Rd, Kingston, RI 02881